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Learning Jupyter

Learning Jupyter

By : Toomey
1 (3)
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Learning Jupyter

Learning Jupyter

1 (3)
By: Toomey

Overview of this book

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more. This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we’ll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode. Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book.
Table of Contents (11 chapters)
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Python pandas in Jupyter

One of the most widely used features of Python is pandas. It is a third-party library of data analysis packages that can be used freely. In this example, we will develop a Python script that uses pandas to see if there is any effect to using it in Jupyter.

I am using the Titanic dataset from http://www.kaggle.com/c/titanic-gettingStarted/download/train.csv. I am sure the same data is available from a variety of sources.

Here is the Python script that we want to run in Jupyter:

from pandas import *
training_set = read_csv('train.csv')
training_set.head()
male = training_set[training_set.sex == 'male']
female = training_set[training_set.sex =='female']
womens_survival_rate = float(sum(female.survived))/len(female)
mens_survival_rate = float(sum(male.survived))/len(male)

The result is we calculate the survival rates of the Titanic's passengers based on their sex.

We create a new notebook, enter the script into appropriate cells, include...

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Learning Jupyter
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